Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study

• Published in: Computer methods and programs in biomedicine Vol. 142; pp. 73 - 79
• Main Authors: Grossi, Enzo, Olivieri, Chiara, Buscema, Massimo
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.04.2017
• Subjects: Adolescent; Algorithms; Artificial neural networks; Autism spectrum disorder; Autism Spectrum Disorder - diagnosis; Autism Spectrum Disorder - physiopathology; Autistic Disorder - diagnosis; Autistic Disorder - physiopathology; Biomarkers - metabolism; Cerebral Cortex - pathology; Child; Computer Simulation; Diagnosis; Diagnosis, Computer-Assisted - methods; EEG; Electroencephalography; Female; Humans; Internal Medicine; Machine Learning; Male; Neural Networks (Computer); Other; Pilot Projects; Software; Diagnosis; EEG; Autism spectrum disorder; Artificial neural networks; artificial neural networks; diagnosis
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2017.02.002

•Autism is a devastating disease affecting 1–2% of newborns. Its incidence is steadily increasing.•A growing body of evidence points out that to effectively treat autism, the earliest possible detection is directly proportional to successful therapy. This clearly implies that the availability of an accurate and relatively inexpensive diagnostic method for early diagnosis should be one of the medical community's highest priorities.•The relevant involvement of the cerebral cortex in substantially altering cortical circuitry explains the unique pattern of deficits and strengths that characterize cognitive functioning. On the other, this makes the potential usefulness of EEG recording plausible as a biomarker of these abnormalities.•Despite this plausibility, very few studies have attempted to use EEG recording in diagnosing autism.•Traditional EEG measures apply frequency domain analysis, assume that EEG is stationary and employ linear feature extraction. Novel approaches based on artificial adaptive systems like those employed by us, apply data driven time& frequency domain analysis, assume that EEG is not stationary and use nonlinear feature extraction.•To our knowledge, this is the first study that applies an artificial adaptive system to extract interesting features in computerized EEG that distinguishes ASD children from typically developing ones.•The results are extremely interesting and open new avenues for confirmatory clinical studies in this important field. Background. Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people. The presence of deviant patterns in simple resting state EEG recordings in autism, consistent with the atypical organization of the cerebral cortex present, prompted us in applying this potent analytical systems in search of a EEG signature of the disease. Aim of the study. The aim of the study is to assess how effectively this methodology distinguishes subjects with autism from typically developing ones. Methods. Fifteen definite ASD subjects (13 males; 2 females; age range 7–14; mean value = 10.4) and ten typically developing subjects (4 males; 6 females; age range 7–12; mean value 9.2) were included in the study. Patients received Autism diagnoses according to DSM-V criteria, subsequently confirmed by the ADOS scale. A segment of artefact-free EEG lasting 60 seconds was used to compute input values for subsequent analyses. MS-ROM/I-FAST coupled with a well-documented evolutionary system able to select predictive features (TWIST) created an invariant features vector input of EEG on which supervised machine learning systems acted as blind classifiers. Results. The overall predictive capability of machine learning system in sorting out autistic cases from normal control amounted consistently to 100% with all kind of systems employed using training-testing protocol and to 84% - 92.8% using Leave One Out protocol. The similarities among the ANN weight matrixes measured with apposite algorithms were not affected by the age of the subjects. This suggests that the ANNs do not read age-related EEG patterns, but rather invariant features related to the brain's underlying disconnection signature. Conclusion. This pilot study seems to open up new avenues for the development of non-invasive diagnostic testing for the early detection of ASD.